Intermediates of Hydrogen Peroxide-Assisted Photooxidation of Salicylic Acid: Their Degradation Rates and Ecotoxicological Assessment
International Journal of Molecular Sciences,
Journal Year:
2025,
Volume and Issue:
26(2), P. 697 - 697
Published: Jan. 15, 2025
Accelerated
photooxidation
of
salicylic
acid
(SA)
was
performed
using
UV
radiation
and
hydrogen
peroxide.
HPLC-MS
analysis
showed
that
the
primary
intermediates
are
2,5-dihydroxybenzoic
acid,
2,3-dihydroxybenzoic
pyrocatechol,
phenol.
Deeper
oxidation
leads
to
low
molecular
weight
aliphatic
acids,
such
as
maleic,
fumaric,
glyoxylic.
The
main
carried
out
in
same
conditions.
degradation
SA
its
follows
first-order
reaction
kinetics.
In
case
irradiation
alone,
photodegradation
is
slightly
faster
(reaction
rate
constant
0.007
min−1)
compared
(0.0052
min−1).
Other
products
degrade
more
slowly
than
SA.
Hydrogen
peroxide,
concentrations
1.8–8.8
mM,
accelerates
intermediate
products.
An
ecotoxicological
evaluation
EPI
SuiteTM
software.
overall
persistence
(POV)
long-range
transport
potential
(LRTP)
all
transformation
were
assessed
OECD
POV
LRTP
screening
tool.
Salicylic
have
toxicity.
Due
their
high
solubility,
these
contaminants
can
travel
considerable
distances
aquatic
environment.
phenol
values
156–190
km.
shorter
(less
100
km).
Language: Английский
Effects of Acetylsalicylic Acid and Biosolids on Edaphic, Vegetative and Biochemical Parameters of Amelichloa caudata Under Water Shortage Conditions
Julio Molina,
No information about this author
Fernando Silva-Romano,
No information about this author
Irina M. Morar
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et al.
Agronomy,
Journal Year:
2025,
Volume and Issue:
15(4), P. 785 - 785
Published: March 23, 2025
Water
scarcity
has
affected
much
of
Chile
for
the
past
15
years,
and
Amelichloa
caudata,
a
native
species
adapted
to
arid
conditions,
may
offer
solution.
The
hypothesis
this
study
is
that
both
acetylsalicylic
acid
(ASA)
biosolids
(BSs)
can
positively
influence
plant
growth
under
water
stress.
This
assessed
effects
ASA
BSs
on
edaphic,
physiological,
biochemical,
productive
parameters
A.
caudata
conditions.
Results
showed
treatments
enhanced
biomass
production,
height,
leaf
number,
canopy
weight.
improved
retention,
mitigating
stress
leading
levels
comparable
controls.
In
contrast,
did
not
show
significant
benefits
had
lowest
values
all
highest
root
dry
weight
was
observed
in
water-restricted
plants,
while
ASA-treated
plants
lower
malondialdehyde
(MDA)
levels,
indicating
reduced
oxidative
However,
BS
treatment
increased
MDA
suggesting
more
severe
damage.
Despite
improvements
high
salt
concentrations
limit
their
effectiveness
further
research
required
optimize
application
rates.
Language: Английский
Performance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset
Guilherme Pires Silva de Almeida,
No information about this author
Leonardo Nazário Silva dos Santos,
No information about this author
Leandro Rodrigues da Silva Souza
No information about this author
et al.
Agronomy,
Journal Year:
2024,
Volume and Issue:
14(10), P. 2194 - 2194
Published: Sept. 24, 2024
One
of
the
most
challenging
aspects
agricultural
pest
control
is
accurate
detection
insects
in
crops.
Inadequate
measures
for
insect
pests
can
seriously
impact
production
corn
and
soybean
plantations.
In
recent
years,
artificial
intelligence
(AI)
algorithms
have
been
extensively
used
detecting
field.
this
line
research,
paper
introduces
a
method
to
detect
four
key
species
that
are
predominant
Brazilian
agriculture.
Our
model
relies
on
computer
vision
techniques,
including
You
Only
Look
Once
(YOLO)
Detectron2,
adapts
them
lightweight
formats—TensorFlow
Lite
(TFLite)
Open
Neural
Network
Exchange
(ONNX)—for
resource-constrained
devices.
leverages
two
datasets:
comprehensive
one
smaller
sample
comparison
purposes.
With
setup,
authors
aimed
at
using
these
datasets
evaluate
performance
models
subsequently
convert
best-performing
into
TFLite
ONNX
formats,
facilitating
their
deployment
edge
The
results
promising.
Even
worst-case
scenario,
where
with
reduced
dataset
was
compared
YOLOv9-gelan
full
dataset,
precision
reached
87.3%,
accuracy
achieved
95.0%.
Language: Английский